This section is intended to introduce various aspects of the art, which may be associated with aspects of the disclosed techniques and methodologies. A list of references is provided at the end of this section and may be referred to hereinafter. This discussion, including the references, is believed to assist in providing a framework to facilitate a better understanding of particular aspects of the disclosure. Accordingly, this section should be read in this light and not necessarily as admissions of prior art.
Decisions made in petroleum or natural gas reservoir development and management can be important to economic results. Development planning includes decisions regarding size, timing and location of production facilities and potential subsequent expansions and connections. The number, location, path allocation to facilities and timing of wells to be drilled and completed in each field can also be important decisions. Reservoir management decisions include operational strategies such as the injection scheme, the allocation of production rates across wells, working over wells, and drilling new wells. It can also be important to evaluate accurately the economic potential of resources for purposes of acquisition or disposition. These decisions/evaluations are greatly complicated by uncertainties, not only the uncertainties of reservoir properties, but also uncertainties in well and facility behavior, and/or economic conditions. A system for helping to make improved decisions for reservoir development and management should address the uncertainties.
Accurate reservoir development and management decisions depend on accurate predictions of reservoir, well, facility and economic behavior (the “system”) in response to those decisions. These predictions rely on estimating the properties impacting the relevant behaviors, and determining the relationship between the properties and the behaviors requires numerical modeling for all but the simplest cases. Improved computer performance increases the amount of detail that can be included in a model, and this increased detail can lead to more accurate predictions of reservoir and fluid behavior, leading to complex, fine-scale (on the order of meters or less) models to represent the key characteristics. On the other hand, the representation of uncertainty drives a need for an ensemble of models to represent the full range of parameter space. At the same time, the optimization of production scenarios may require use of representative, but much-faster-running models, which are necessarily less detailed, given the state of computing technology. Thus there is a simultaneous need for both detailed (“high-fidelity”) models and high-speed models. If the high-speed models can be calibrated and linked to the high-fidelity models, the needs for both accuracy and speed might be met. However, generating and calibrating the models may pose a problem. This problem is amplified by the presence of uncertainty because, in that case, the uncertainty representation also has to be calibrated between the high-speed representation with many models and the high-fidelity representation with few models—and propagated between these levels. Therefore, there is a need for a modeling system that is both accurate and fast, so that development planning and reservoir management decisions can be made reliably and quickly. The accuracy is ultimately determined in terms of the relevant expected (weighted averages over all possible uncertain outcomes) flow response of the reservoirs as functions of the conditions and controls applied to them.
In existing methods the model inputs tend to be treated in an ad hoc fashion. Seismic data are used to define the structure of a subsurface region, geologic information is used to construct layers and their properties, and so on. Normally the model is adapted to current needs based on intuition and experience. When different individuals work on different aspects of the physics, different models are built that then need to be combined into a single model. Although there are some software applications that permit integrated modeling of reservoir and facilities (Beckner et al), much work in this area has focused on bringing the physics models together. A more systematic approach to the modeling, including all the relevant physics and uncertainties, is needed.
In complex circumstances it can be difficult to formulate the question being posed in such a way that even a single, deterministic model can be run in a reasonable amount of time. However, engineers with sufficient experience and judgment can usually, with enough effort, eventually find a way to build a good model or to build a model they can adjust/correct to determine a sufficiently accurate result. To explore uncertainty space it may be required to build a large number (hundreds, thousands, or more) of models, but these can be “farmed out” to a large number of central processing units (CPUs) and solved separately.
On the other hand, optimization technologies typically have very poor performance-to-optimization-problem-size (e.g., number of decisions to determine) characteristics—usually with geometric or even exponential growth. And optimization technologies normally require that model results be generated in a large number of cases (hundreds, thousands, or more). So when applying optimization technology even for a single, deterministic case, it may be helpful for the model representing the system to be optimized to run very fast, such as less than one CPU second. In either optimization or uncertainty assessment, accurate but fast models can be helpful to getting the right answer. When trying to assess both optimization and uncertainty, fast models are helpful in all but the most trivial cases. FIG. 1 shows a graphical representation of the trade-off between model detail (measured along horizontal axis 12) and uncertainty detail (measured along vertical axis 14). The angled line 16 represents the limit of computational capability. The position of the angled line depends on the computing system being used. FIG. 1 illustrates that increasing model detail (along horizontal axis 12) limits the range of uncertainty that can be modeled (along vertical axis 14), and that increasing uncertainty detail limits the physical detail that can be modeled explicitly.
To reduce the computational requirements of reservoir flow modeling, upscaling may be used to link coarse (i.e., fast) models with finer-scale models, and in particular, to link reservoir-geology models (static rock and fluid models) to reservoir-flow models. Upscaling consists of determining coarse-scale properties that provide some level of fidelity to fine-scale properties. Even for single-phase flow (permeability upscaling), the upscaling problem is not fully resolved. Simple or even complex averaging techniques suffer from flaws due to the geometric complexity of real rocks. Flow-based approaches may be better to use and are not excessively computationally difficult as long as methods with substantial localization can be used (see Khan and Dawson (2000), Stern and Dawson (1999)). However, for multiphase flow, upscaling methods can be problematic. Classical techniques developed to overcome limitations on computational speed lead to models whose behavior depends strongly on the assumed flows. Furthermore, using measured rock properties to represent model behavior can be flawed on two grounds. First, the region to be modeled or represented usually consists of multiple rock types. Second, fluid flow within the region rarely is uniform within the region. More recent upscaling methods for multiphase flow have been developed to handle this situation (See Jenny et al., Zhou et al. (1997)). These methods basically involve embedding a fine scale solution in the coarse scale. The fine scale model is retained in its original form or in another form and used to compute local flow behavior. However, these methods tend to be expensive in terms of computing time and still do not adequately solve how the properties of the coarse scale are determined.
Systematic errors in model behavior at coarser scales have not been widely recognized. Some initial work has been done by Christie et al (2008), but its comparison of tank models to very coarse models may be insufficient for many applications. Determining systematic error across all scales may be needed for proper validation and calibration of a model.
The methods described above implicitly or explicitly assume that the fine-scale model is deterministic. For the levels of uncertainty commonly found in reservoir models, including uncertainty in the system to be modeled may complicate the ability to arrive at an accurate model. The most commonly used approach is to create a small number (often just one or two) of additional models that are thought to represent key uncertainties in the system and to work the reservoir engineering or development planning problem for each of these cases.
Recent efforts have been made to be more thorough in representing uncertainty, for example by developing a series of single-property distribution diagrams or two-property cross-correlation diagrams. However, the actual geology, geophysics, and geochemistry found in reality is necessarily more complex.
Unless data are lost, uncertainty resolves over time. Thus, true uncertainty should be monotonically decreasing. However, the perceived uncertainty may suffer increases as unexpected information about the reservoir is learned. The foregoing refers to “total” (field-wide) uncertainty. Local uncertainty (in a particular region of space) can remain large for bypassed regions late into the life of a reservoir. If neighboring regions are developed and thus the properties of those regions become well-known, the uncertainty in adjacent bypassed regions will be a strong function of the quality of the structure (normally estimated through seismic data) and the extent to which properties in the neighboring region can be correlated into the bypassed region.
Previous attempts to model complex development planning or reservoir management systems focus on linking (but not fully integrating) reservoir and facility models. Such efforts are largely unnecessary when a fundamentally integrated approach is used, as described in Beckner et al. (2001). Use of a linked (not integrated) modeling approach would make Hierarchical Modeling difficult, but not impossible, to apply.
The following references may be relevant.    U.S. Pat. No. 7,373,251 B2 to Hamman, et al.    U.S. Pat. No. 7,254,091 B1 to Gunning, et al.    U.S. Pat. No. 6,826,520 to Khan, et al.    U.S. Patent Application No. US2008/0133550 A1 to Orangi, et al.    U.S. Patent Application No. US2007/0299643 A1 to Guyaguler, et al.    U.S. Patent Application No. US2007/0265815 to Couet, et al.    Patent Publication WO2004046503 A1 to Kosmala, et al.    Patent Publication WO2001027858 A1 to Anderson, et al.    Schulze-Riegert, R., Ghedan, S., “Modern Techniques for History Matching”; 9th International Symposium on Reservoir Simulation, Abu Dhabi (2007)    Frykman, P., and Deutsch, C. V., “Practical Application of Geostatistical Scaling Laws for Data Integration”, Petrophysics 43(3), May-June 2002, pp 153-171 (2002).    Monfared, H., Christie, M., Pickup, G., “A Critical Analysis of Upscaling”, 13th Congress of the Research Inst. of Petroleum Industry (National Iranian Oil Co.) (2007)    Jenny, P., Lee, S. H., and Tchelepi, H. A., “Adaptive Multiscale Finite-Volume Method for Multi-Phase Flow and Transport in Porous Media”, Multiscale Model. Simul. 3(1) pp 50-64 (2004).    Zhou, H., and Tchelepi, H. A., “Operator Based Multiscale Method for Compressible Flow”, SPE106254 presented at the 2007 SPE Reservoir Simulation Symposium, Feb. 26-28, 2007, Houston, Tex.    Beckner, B. L., Hutfilz, J. M., Ray, M. B., Tomich, J. F., “EMpower: ExxonMobil's New Reservoir Simulation System”, 2001 SPE Middle East Oil Show Bahrain, March 2001.    Stern, D., Dawson, A. G., “A Technique for Generating Reservoir Simulation Grids Preserving Geologic Heterogeneity”, 1999 SPE Reservoir Simulation Symposium, Houston, Tex.    Christie, M. A., Pickup, G. E., O'Sullivan, A. E., Demanyov V., “Use of Solution Error Models in History Matching”, 11th European Conference on Mathematics of Oil Recovery, Bergen, Norway, Sep. 8-11, 2008.    Scheidt, C., Zabalza-Mezghani, I., “Assessing Uncertainty and Optimizing Production Schemes—Experimental Designs for Non-Linear Production Response Modeling and Application to Early Water Breakthrough Prevention” 9th European Conference on Mathematics of Oil Recovery, (IFP) Cannes, France, Aug. 30-Sep. 2, 2004.    Caers, J., Park, K., “A Distance-based Representation of Reservoir Uncertainty: the Metric EnKF”, 11th European Conference on Mathematics of Oil Recovery, Bergen, Norway, Sep. 8-11, 2008.